ASSESSING THE EFFICIENCY AND RISKS OF USING AI IN COMPANY FINANCIAL ACTIVITIES

Abstract

This article examines the theoretical and methodological approaches to utilizing artificial intelligence (AI) in the financial activities of business entities, evaluating its impact and priority areas within corporate financial management. The study analyzes efficiency gains–such as profit growth and cost reduction–and risks associated with AI deployment, assessing their subsequent effects on the financial positions and future prospects of domestic enterprises.
Particular focus is placed on the analytical tools used to evaluate the economic effects of AI. The paper scrutinizes the advantages and disadvantages of peer-group analysis, machine-learning-based predictive modeling, and surveys of financial executives regarding organizational indicators across various integration stages. Methodologically, the study argues that rather than comparing disparate market competitors, it is more economically viable to analyze a single enterprise’s performance «before and after» AI implementation, excluding the initial integration and employee adaptation phases.
Furthermore, a comprehensive framework for assessing AI’s impact on corporate financial performance is provided through indicators categorized into four interconnected analytical subsystems. The study identifies key capabilities enabled by AI, including accelerated data processing, minimized routine operations, dynamic production budget adjustments, and enhanced forecasting accuracy for working capital requirements. Concurrently, the paper addresses emerging threats, including cybersecurity vulnerabilities, programming logic errors, low data quality, and managers’ over-reliance on digital solutions. Ultimately, the comprehensive assessment of these efficiencies and risks is substantiated as a matter of paramount relevance.